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Analysis and simulation of Optical Networks Xin Liu Outline • Analytic Approach Probability: Expectation values, Variance Network Global Expectation Model Stochastic Process: Markov chain Packet Delay in OCS networks • Simulation Discrete Event Simulation Model OCS and OBS extension on NS Analytic Approach • Methods: formal derivation, considered approximation, semi-empirical observation. • Intent: To formulate analytic or closed form; To complement, not supplant more accurate, but computationally intensive tools based on numerical simulation. Simulation • Methods: To implement discrete event simulation model using generic languages; To extend known simulation platform. • Intent: To be close to the real network. Network Global Expectation Model • Key idea: Use expectation values to describe required quantities of key network and network element resources. • Significance: Provide approximate results for the preliminary evaluation and design of dynamic networks. • Assumption: single-tier backbone networks, location-independent traffic demands. Network Global Expectation Model CT vi ci the total network cost the number of network elements of type i the unit cost of network element of type i. CT c i i CT v c i i i Challenge: vi Network Global Expectation Model • Expectation value 1 q m m q i i 1 • Example: L is the number of links and N is the number of nodes. CT L cl N cn Primary Model Variables (Input) • Network graph G( L, N ) [ gij ] adjacent matrix • Network traffic T : the total ingress/egress traffic D : the number of demands [dij ] : demand matrix Primary Model Variables • Specify the difference between one-way and two-way links Links: L L2 L1 2 Total Traffic: T T2 T1 2 Total Demands: D D2 D1 2 Output • Number of Demands D1 N d ij N d n i, j • Traffic Demand Bit-Rate T1 T2 D1 D2 • Degree of Node L1 N Output • Number of Hops 1 well known a demand model [ gij ] [hij ] h D D h ij i j Number of Hops N 1 nodes 4 h N 2 1 square root relation Divide the network into 4 sectors centered on the selected node Number of Hops The Moore bound results from the construction of a tree whose root is the parent of vertices and each subsequent vertex is itself the parent of 1 vertices. max 1 max 1 Logarithmic relation 0-hop 1-hop 2-hops D=3-hops Number of Hops n 1 max D ( max 1) h 1 1 max h 1 Dmin ( max 1) D 1 max 2 max 2 ln 1 (n 1) max ln( max 1) 2 ln 1 (n 1) h ln( 1) Output • Demands on Link 1 o W L D h d h 1 hij L i j D • Restoration Capacity W k W o (1 k ) ab k b Inverse dependency upon the degree of the nodes Output • Traffic on Link D T W h h L L • Number of Ports P PADD PDROP PTHRU Number of ports Drop+Thru Add Add+Thru Drop d PADD PDROP d PTHRU d ( h 1) d Packet Delay in OCS Networks • The paper first presents the queue length distribution and the packet delay distribution in a single logical buffer of the edge router, and then extends that discussion to a network of edge routers. • To ensure computational tractability, the framework approximates the evolution of each buffer independently. Model Formulation • A circuit is a unidirectional lightpath connecting a pair of source-destination edge routers capable of transmitting C b/s uninterruptedly for a period of T seconds. • Circuits are allocated to the logical buffers using a policy R based on the queue lengths at all logical buffers. Model Formulation • Consider J data streams, each associated with a source-destination pair of edge routers, Qos class, a route and wavelength assignment sequence from the source to the destination, and other external classifications. • So there are J logical buffers. Model Formulation • Normalized lightpath arrival rates { Aj ,1 j J } • Normalized lightpath transmission rates K • Circuit switching decision epoch n Model Formulation • The queue length in logical buffer j at epoch n X j ( n) • The system state at epoch n X(n) ( X1 (n), X 2 (n), , X J (n)) • A binary vector indicating which of the logical buffers are allocated circuits at state X(n) δ R ( x) (1R ( x), 2R ( x), , JR ( x)) Mathematical Model • The process (X(n), n 0) is a Markov chain. • But each X j (n) is not a Markov chain. X j (n 1) [ X j (n) Aj jR (X(n)) K ] • Let j (i, n) be the probability that algorithm R allocates a circuit to buffer j with length i at epoch n. [ i A K ] , with probability j (i, n); j X j (n 1) with probability 1 j (i, n); i Aj , Simulation • Discrete Event Simulation Model. • OCS and OBS extension on NS. Discrete Event Simulation Model INIT()Initialize System 1. simulation timer 2. system status 3. event list 4. performance statistics TIMING()Timing control 1. Schedule events according to the event list, return the next event to happen 2. Modify timer EVENT()Handle different events from TIMING() 1. Modify system status 2. Modify performance statistics 3. Get the time of the next event and add it into event list N Simulation Over Y 1. Obtain performance statistics 2. Print results Event handling • Accept Execute RWA for connection requests; Modify the number of arriving requests, the number of successfully established working path; Modify the information of network resource. Create the next event according to assumed distribution and append it into event list. • Service Over Release the resource of working channel which is not alive. Basic Modules • Phy-Topo : Generate physical topology, such as TORUS, NSFNet. • Routing : Implement known routing algorithms, such as Dijkstra’s Algorithm, Floyd-based SPF, K-Shortest-With-Loop-Path. • Graph Theory Algorithm : provide basic graph theory algorithms, such as MaxFlow, MinCost-Flow. • Survival : provides protection and restoration schemes. • Resource : Different policies, such as routing, wavelength assignment, control management, survivability schemes, will lead to different efficiency in resource usage. • Wave-Assign : Combined with routing Module, it completes the RWA function in WDM networks. • V-Topo : This module controls the virtual topology in IP layer. • Traffic : It contains Poisson, Gaussian, Self-Similar traffic module. It is used to generate the random sequence of connection requests. • Pseudo-Random Number : Generate random number in (0, 1) uniformly. • DES : discrete event simulation module. • Performance Metrics Statistic : In each DES process, track interested statistics variables. After simulation is over, prints out the values of performance metrics. Basic Modules Wave-Assign Virtual Topology Graph Theory Traffic Arrive Routing PseudoRandom Number Link-FailureOccur Physical Topology Timing Link-FailureOver Survival Performance Metrics Statistic Server Over Resource OBS extension on NS • OBS-ns (UMBC) Use centralized structure to assign resource; Add new classes for new types; Ignore the architecture of NS. • OBS-extension Keep to the distributed architecture of NS; Add new component in existing composite classes for new features. OBS-extension Task • WDM link extension No multi-channel link model in NS; To add a multi-server queue in normal link model. • Assembly Module in Ingress Nodes of OBS Networks • Signaling, Qos and contention resolution Normal Link Model Link head_ enqT_ queueT_ drophead_ deqT_ linkT_ ttlT_ revT_ drpT_ head_ The entry of a link queue_ The queue reference of a link link_ The reference of a link with delay and bandwidth property ttl_ The reference of TTL management drophead_ The reference of the head of the drop queue WDM link extension WvAssign WvAssign : Queue WaveClassifier : Classifier Wavelength classifier queueT_ Wavelength Classifier Receive packet Process packet head Get Wavelength number Support Wavelength conversion N Y Execute WA algorithm Wavelength available N Y Any port available N Y return -1 Modify packet head return wavelength number return wavelength number return -1 WDM link extension Link WvAssign head_ enqT_ deqT_ linkT_ ttlT_ revT_ queueT_ drophead_ drpT_ #Create WDM link $ns duplex-link $n3 $n4 1Mb 20ms WvAssign 4 FirstFit 1 OBS extension • Redirector Redirecting table and redirecting buffer. Similar to route table and cache in traditional router. • Assembly Agent Set assembly scheme, parameters and signaling . OBS extension CBR NULL UDP Disssembly IP router Assembly WDM core Ingress OBS Packet flow Burst flow egress Assembly agent Port classifier Address classifier Assembly Redirector OBS ingress node WDM link Packet flow Burst flow WDM link Test Reference • • • • • Steven K. Korotky, “Network Global Expectation Model: A Statistical Formalism for Quickly Quantifying Network Needs and Costs”, Journal of Lightwave Technology Preprint, 2004. Zvi Rosberg, “Packet Delay in Optical Circuit-Switched Networks”, 2004. Zvi Rosberg, “Analysis of OBS Networks with Limited Wavelength Conversion”, 2004. Jean-Francois Labourdette, “Fast Approximate Dimensioning and Performance Analysis of Mesh Optical Networks”, Design of Reliable Communication Networks 2003, 428-438. Damon J. Wischik, “Mathematical Modeling of Optical Burst-Switched (OBS) Networks”, 2004.